Workshop on Machine Learning for cryoEM

(18 Oct 2021–29 Oct 2021)

Organizing Committee

  • Hui Ji  (National University of Singapore)
  • Ardan Patwardhan  (European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI))

Contact Information

General Enquiries: ims(AT)nus.edu.sg
Scientific Aspects Enquiries: duaneloh(AT)nus.edu.sg

Overview

Cryo-electron microscopy (cryo-EM) has become a major imaging modality for atomic resolution structural biology. Early optimism around cryo-EM ability to resolve the atomic structures of nanometer macromolecular machines that are not amenable to other imaging techniques. Furthermore, the related technique of cryo-electron tomography (cryo-ET) has also become indispensable for examining the organization and macromolecular structure of cells at nanometer resolution. The far-reaching prospect, however, comes from the possibility of both techniques to resolve the structure and dynamics of macromolecular machines to near-atomic resolution.

Currently the cryo-EM community faces unprecedented data loads where the only practical way to analyze them requires a heavy reliance on specialized processing algorithms. Recent breakthroughs in machine learning have created opportunities to augment these algorithms with human intuition, complex prior knowledge, and robust validation schemes.

This unique IMS-sponsored workshop gathers international specialists in cryo-EM, machine learning, and mathematical sciences to share and inspire future applications of machine learning in cryo-EM.

Activities

TBA

 

List of Participants

Hui ji National University of Singapore, Singapore
Duane Loh National University of Singapore, Singapore
Steven J Ludtke Baylor College of Medicine, USA
Paul Matsudaira National University of Singapore, Singapore
Ardan Patwardhan European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), UK
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